Part Two of Research Road Map on Medical Imaging Artificial Intelligence Published in the Journal of the American College of Radiology (JACR)

RESTON, Va., May 28, 2019 /PRNewswire/ — Today, the Journal of the American College of Radiology (JACR®) published a report detailing real-world artificial intelligence (AI) challenges and summarizing the priorities for translational research in AI for medical imaging to help accelerate the safe and effective use of AI in clinical practice. The four key priorities outlined include:

  1. creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI;
  2. establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias;
  3. establishing tools for validation and performance monitoring for AI algorithms to facilitate regulatory approval; and
  4. developing standards and common data elements for seamless integration of AI tools into existing clinical workflows.

As part of a multi-stakeholder approach, part one of the road map published in Radiology outlined the challenges, opportunities and priorities for foundational research in AI for medical imaging. The two reports are the outcome of an August 2018 workshop convened by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at the National Institutes of Health (NIH) in Bethesda, Md., to explore the future of AI in medical imaging. The NIH, the American College of Radiology (ACR), the Radiological Society of North America (RSNA) and The Academy for Radiology and Biomedical Imaging Research (The Academy) co-sponsored the workshop.

“Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower because we must ensure AI in medical imaging is useful, safe, effective and easily integrated into existing radiology workflows before they can be used in routine patient care,” said Bibb Allen, MD, workshop co-chair and chief medical officer of the ACR Data Science Institute®. “The workshop highlighted structured AI use case development, access to diverse sources of data for training AI models, multi-site algorithm validation and monitoring the performance of these models using real-world data from clinical use as ways to accelerate the widespread deployment and clinical use of AI algorithms to improve the care we provide our patients.”

“Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,” said Krishna Kandarpa, MD, PhD, co-author of the report and director of research sciences and strategic directions at NIBIB. “This roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies that will help us take the best advantage of AI technologies as they impact the medical imaging field.”

“Our companion paper gave a roadmap to advance foundational machine learning research. But for foundational research to benefit patients, novel algorithms must be evaluated and deployed in a safe and effective manner. This new roadmap paper gives guidance for the clinical translation of AI innovation,” said Curtis P. Langlotz, MD, PhD, report co-author and RSNA board liaison for information technology and annual meeting. “Together, these two connected roadmaps show us how AI not only will transform the work of radiologists and other medical imagers, but also will enhance the delivery of care throughout the clinical environment.”

“This NIBIB-sponsored workshop was an important step in coordinating private and government efforts related to AI implementation in medical imaging. It will take a true public-private partnership to realize the tremendous potential contribution of AI to transform medical imaging, and this roadmap is the first step in that direction,” said Mitchell Schnall, MD, PhD, Eugene P. Pendergrass Professor & chair of radiology, University of Pennsylvania; vice president & DxCP task force chair, Academy of Radiology & Biomedical Imaging Research.

The workshop organizers look forward to continuing their work together — along with developers, regulatory agencies and the public — to ensure deployment of medical imaging AI in such a way that the end users can be confident that the algorithm output is accurate, free of unintended bias and safe for patients.

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